A machine-learning regional clustering approach to understand ventilator-induced lung injury: a proof-of-concept experimental study.

ABSTRACT:

Background

The spatiotemporal progression and patterns of tissue deformation in ventilator-induced lung injury (VILI) remain understudied. Our aim was to identify lung clusters based on their regional mechanical behavior over space and time in lungs subjected to VILI using machine-learning techniques.

Results

Ten anesthetized pigs (27 ± 2 kg) were studied. Eight subjects were analyzed. End-inspiratory and end-expiratory lung computed tomography scans were performed at the beginning and after 12 h of one-hit VILI model. Regional image-based biomechanical analysis was used to determine end-expiratory aeration, tidal recruitment, and volumetric strain for both early and late stages. Clustering analysis was performed using principal component analysis and K-Means algorithms. We

SUBMITTER: Cruces P 

PROVIDER: S-EPMC11220131 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature